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Found 14 papers, 3 papers with code
GaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting
Extensive experimental results on generating million-level point cloud data validate the effectiveness of our method, substantially improving the quality of colored point clouds and demonstrating significant potential for applications involving large-scale point clouds in autonomous robotics and human-robot interaction scenarios.
An Explainable Non-local Network for COVID-19 Diagnosis
The network is embedded with a nonlocal module to capture global information, while a 3D attention module is embedded to focus on the details of the lesion so that it can directly analyze the 3D lung CT and output the classification results.
U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation
Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets.
ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Features
Based on the ALIF pipeline, we present the ALIF-OTL and ALIF-OTA schemes for launching attacks in both the digital domain and the physical playback environment on four commercial ASRs and voice assistants.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Chest-Diffusion: A Light-Weight Text-to-Image Model for Report-to-CXR Generation
However, the large distribution difference between medical reports and natural texts, as well as high computational complexity in common stable diffusion limit the authenticity and feasibility of the generated medical images.
Robustly Optimized Deep Feature Decoupling Network for Fatty Liver Diseases Detection
This can lead to significant differences in recognition accuracy between classes and obvious recognition weaknesses.
MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition
Recent few-shot action recognition (FSAR) methods typically perform semantic matching on learned discriminative features to achieve promising performance.
Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation
Especially, an asymmetric learning framework is developed by extending the aspect ratio annotations with two types of pseudo labels, i. e., conservative labels and radical labels, to train two asymmetric segmentation networks simultaneously.
Learning to Drive Anywhere
Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e. g., left vs. right-hand traffic.
Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks
The language models, especially the basic text classification models, have been shown to be susceptible to textual adversarial attacks such as synonym substitution and word insertion attacks.
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services
Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures.
GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers
Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques.
Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping
With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community.
Hawking radiation of a multi-fractional Schwarzschild black hole
Following the initial work of Calcagni et al. on the black holes in multi-fractional theories, we focus on the Schwarzschild black hole in multi-fractional theory with q-derivatives.
General Relativity and Quantum Cosmology
